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A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Author

Listed:
  • Simon Nam Thanh Vu

    (DTU Compute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
    These authors contributed equally to this work.)

  • Mads Stege

    (DTU Compute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
    These authors contributed equally to this work.)

  • Peter Issam El-Habr

    (DTU Compute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
    These authors contributed equally to this work.)

  • Jesper Bang

    (DTU Compute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
    These authors contributed equally to this work.)

  • Nicola Dragoni

    (DTU Compute, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
    Current address: Richard Petersens Plads, 2800 Kgs. Lyngby, Denmark.)

Abstract

Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

Suggested Citation

  • Simon Nam Thanh Vu & Mads Stege & Peter Issam El-Habr & Jesper Bang & Nicola Dragoni, 2021. "A Survey on Botnets: Incentives, Evolution, Detection and Current Trends," Future Internet, MDPI, vol. 13(8), pages 1-43, July.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:8:p:198-:d:605825
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    References listed on IDEAS

    as
    1. Ruidong Chen & Weina Niu & Xiaosong Zhang & Zhongliu Zhuo & Fengmao Lv, 2017. "An Effective Conversation-Based Botnet Detection Method," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, April.
    2. Xuan Dau Hoang & Quynh Chi Nguyen, 2018. "Botnet Detection Based On Machine Learning Techniques Using DNS Query Data," Future Internet, MDPI, vol. 10(5), pages 1-11, May.
    3. Georgios Spathoulas & Nikolaos Giachoudis & Georgios-Paraskevas Damiris & Georgios Theodoridis, 2019. "Collaborative Blockchain-Based Detection of Distributed Denial of Service Attacks Based on Internet of Things Botnets," Future Internet, MDPI, vol. 11(11), pages 1-24, October.
    4. Ahmad Karim & Rosli Salleh & Muhammad Khurram Khan, 2016. "SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-35, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    botnet; malware; security; IoT;
    All these keywords.

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